{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predict Insample\n",
    "> Tutorial on how to produce insample predictions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial provides and example on how to use the `predict_insample` function of the `core` class to produce forecasts of the train and validation sets. In this example we will train the `NHITS` model on the AirPassengers data, and show how to recover the insample predictions after model is fitted.\n",
    "\n",
    "*Predict Insample*: The process of producing forecasts of the train and validation sets.\n",
    "\n",
    "*Use Cases*: \n",
    "* Debugging: producing insample predictions is useful for debugging purposes. For example, to check if the model is able to fit the train set.\n",
    "* Training convergence: check if the the model has converged.\n",
    "* Anomaly detection: insample predictions can be used to detect anomalous behavior in the train set (e.g. outliers). (Note: if a model is too flexible it might be able to perfectly forecast outliers)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/PredictInsample.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Installing NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Loading AirPassengers Data\n",
    "\n",
    "The `core.NeuralForecast` class contains shared, `fit`, `predict` and other methods that take as inputs pandas DataFrames with columns `['unique_id', 'ds', 'y']`, where `unique_id` identifies individual time series from the dataset, `ds` is the date, and `y` is the target variable. \n",
    "\n",
    "In this example dataset consists of a set of a single series, but you can easily fit your model to larger datasets in long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from neuralforecast.utils import AirPassengersDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-01-31</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-02-28</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-03-31</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-04-30</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-05-31</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds      y\n",
       "0        1.0 1949-01-31  112.0\n",
       "1        1.0 1949-02-28  118.0\n",
       "2        1.0 1949-03-31  132.0\n",
       "3        1.0 1949-04-30  129.0\n",
       "4        1.0 1949-05-31  121.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_df = AirPassengersDF # Defined in neuralforecast.utils\n",
    "Y_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Model Training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we train the `NHITS` models on the AirPassengers data. We will use the `fit` method of the `core` class to train the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import NHITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "horizon = 12\n",
    "\n",
    "# Try different hyperparmeters to improve accuracy.\n",
    "models = [NHITS(h=horizon,                      # Forecast horizon\n",
    "                input_size=2 * horizon,         # Length of input sequence\n",
    "                max_steps=1000,                 # Number of steps to train\n",
    "                n_freq_downsample=[2, 1, 1],    # Downsampling factors for each stack output\n",
    "                mlp_units = 3 * [[1024, 1024]]) # Number of units in each block.\n",
    "          ]\n",
    "nf = NeuralForecast(models=models, freq='M')\n",
    "nf.fit(df=Y_df, val_size=horizon)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Predict Insample"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `NeuralForecast.predict_insample` method you can obtain the forecasts for the train and validation sets after the models are fitted. The function will always take the last dataset used for training in either the `fit` or `cross_validation` methods.\n",
    "\n",
    "With the `step_size` parameter you can specify the step size between consecutive windows to produce the forecasts. In this example we will set `step_size=horizon` to produce non-overlapping forecasts.\n",
    "\n",
    "The following diagram shows how the forecasts are produced based on the `step_size` parameter and `h` (horizon) of the model. In the diagram we set `step_size=2` and `h=4`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](../imgs_indx/predict_insample.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 37.76it/s]\n"
     ]
    }
   ],
   "source": [
    "Y_hat_insample = nf.predict_insample(step_size=horizon)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `predict_insample` function returns a pandas DataFrame with the following columns:\n",
    "* `unique_id`: the unique identifier of the time series.\n",
    "* `ds`: the datestamp of the forecast for each row.\n",
    "* `cutoff`: the datestamp at which the forecast was made.\n",
    "* `y`: the actual value of the target variable.\n",
    "* `model_name`: the forecasted values for the models. In this case, `NHITS`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>cutoff</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-01-31</td>\n",
       "      <td>1948-12-31</td>\n",
       "      <td>0.204289</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-02-28</td>\n",
       "      <td>1948-12-31</td>\n",
       "      <td>0.302111</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-03-31</td>\n",
       "      <td>1948-12-31</td>\n",
       "      <td>0.399522</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-04-30</td>\n",
       "      <td>1948-12-31</td>\n",
       "      <td>0.429369</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1949-05-31</td>\n",
       "      <td>1948-12-31</td>\n",
       "      <td>0.518200</td>\n",
       "      <td>121.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   unique_id         ds     cutoff     NHITS      y\n",
       "0        1.0 1949-01-31 1948-12-31  0.204289  112.0\n",
       "1        1.0 1949-02-28 1948-12-31  0.302111  118.0\n",
       "2        1.0 1949-03-31 1948-12-31  0.399522  132.0\n",
       "3        1.0 1949-04-30 1948-12-31  0.429369  129.0\n",
       "4        1.0 1949-05-31 1948-12-31  0.518200  121.0"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_insample.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "The function will produce forecasts from the first timestamp of the time series. For these initial timestamps, the forecasts might not be accurate given that models have very limited input information to produce forecasts.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Plot Predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the forecasts for the train and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(Y_hat_insample['ds'], Y_hat_insample['y'], label='True')\n",
    "plt.plot(Y_hat_insample['ds'], Y_hat_insample['NHITS'], label='Forecast')\n",
    "plt.axvline(Y_hat_insample['ds'].iloc[-12], color='black', linestyle='--', label='Train-Test Split')\n",
    "plt.xlabel('Timestamp [t]')\n",
    "plt.ylabel('Monthly Passengers')\n",
    "plt.grid()\n",
    "plt.legend()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Note how the forecasts for the train set are very accurate, while the forecast in the validation set (last 12 timetamps), are less precise. This is because the model was trained on the train set, and deep learning models such as the `NHITS` can easily overfit the train set.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
